def test_log_vector_histogram():
# Construct a minimal SPN.
h1 = Histogram([0., 1., 2.], [0.25, 0.75], [1, 1], scope=0)
h2 = Histogram([0., 1., 2.], [0.45, 0.55], [1, 1], scope=1)
h3 = Histogram([0., 1., 2.], [0.33, 0.67], [1, 1], scope=0)
h4 = Histogram([0., 1., 2.], [0.875, 0.125], [1, 1], scope=1)
p0 = Product(children=[h1, h2])
p1 = Product(children=[h3, h4])
spn = Sum([0.3, 0.7], [p0, p1])
inputs = np.column_stack((
np.random.randint(2, size=30),
np.random.randint(2, size=30),
)).astype("float64")
if not CPUCompiler.isVectorizationSupported():
print("Test not supported by the compiler installation")
return 0
# Execute the compiled Kernel.
results = CPUCompiler(maxTaskSize=5).log_likelihood(spn, inputs, supportMarginal=False)
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(spn, inputs)
reference = reference.reshape(30)
# Check the computation results against the reference
# Check in normal space if log-results are not very close to each other.
assert np.all(np.isclose(results, reference)) or np.all(np.isclose(np.exp(results), np.exp(reference)))
def test_cpu_histogram():
# Construct a minimal SPN.
h1 = Histogram([0., 1., 2.], [0.25, 0.75], [1, 1], scope=0)
h2 = Histogram([0., 3., 6., 8.], [0.35, 0.1, 0.55], [1, 1], scope=1)
h3 = Histogram([0., 1., 2.], [0.33, 0.67], [1, 1], scope=0)
h4 = Histogram([0., 5., 8.], [0.875, 0.125], [1, 1], scope=1)
p0 = Product(children=[h1, h2])
p1 = Product(children=[h3, h4])
spn = Sum([0.3, 0.7], [p0, p1])
inputs = np.column_stack((
np.random.randint(2, size=30),
np.random.randint(8, size=30),
)).astype("float64")
# Insert some NaN in random places into the input data.
inputs.ravel()[np.random.choice(inputs.size, 5, replace=False)] = np.nan
if not CUDACompiler.isAvailable():
print("Test not supported by the compiler installation")
return 0
# Execute the compiled Kernel.
results = CUDACompiler().log_likelihood(spn, inputs)
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(spn, inputs)
reference = reference.reshape(30)
# Check the computation results against the reference
# Check in normal space if log-results are not very close to each other.
assert np.all(np.isclose(results, reference)) or np.all(
np.isclose(np.exp(results), np.exp(reference)))
def _deserialize_histogram(self, node, node_map):
breaks = node.hist.breaks
densities = node.hist.densities
reprPoints = node.hist.binReprPoints
type = enum2Type.get(node.hist.type)
metaType = enum2MetaType.get(node.hist.metaType)
hist = Histogram(breaks=breaks, densities=densities, bin_repr_points=reprPoints, scope=node.hist.scope,
type_=type, meta_type=metaType)
hist.id = node.id
return hist
def __init__(self,
breaks,
densities,
bin_repr_points,
scope=None,
type_=None,
meta_type=MetaType.UTILITY):
# has same member variables as histogram
Histogram.__init__(self,
breaks,
densities,
bin_repr_points,
scope,
type_=None,
meta_type=MetaType.UTILITY)
def histogram_tree_to_spn(tree, features, obj_type, tree_to_spn):
node = Histogram(list(map(float, tree.children[1].children)), list(map(float, tree.children[2].children)))
feature = str(tree.children[0])
node.scope.append(features.index(feature))
return node
def test_binary_serialization_roundtrip(tmpdir):
"""Tests the binary serialization for SPFlow SPNs by round-tripping
a simple SPN through serialization and de-serialization and comparing
the graph-structure before and after serialization & de-serialization."""
h1 = Histogram([0., 1., 2.], [0.25, 0.75], [1, 1], scope=1)
h2 = Histogram([0., 1., 2.], [0.45, 0.55], [1, 1], scope=2)
h3 = Histogram([0., 1., 2.], [0.33, 0.67], [1, 1], scope=1)
h4 = Histogram([0., 1., 2.], [0.875, 0.125], [1, 1], scope=2)
p0 = Product(children=[h1, h2])
p1 = Product(children=[h3, h4])
spn = Sum([0.3, 0.7], [p0, p1])
model = SPNModel(spn, featureValueType="uint32")
query = JointProbability(model)
binary_file = os.path.join(tmpdir, "test.bin")
print(f"Test binary file: {binary_file}")
BinarySerializer(binary_file).serialize_to_file(query)
deserialized = BinaryDeserializer(binary_file).deserialize_from_file()
assert (isinstance(deserialized, JointProbability))
assert (deserialized.batchSize == query.batchSize)
assert (deserialized.errorModel.error == query.errorModel.error)
assert (deserialized.errorModel.kind == query.errorModel.kind)
assert (deserialized.graph.featureType == model.featureType)
assert (deserialized.graph.name == model.name)
deserialized = deserialized.graph.root
assert get_number_of_nodes(spn) == get_number_of_nodes(deserialized)
assert get_number_of_nodes(spn,
Sum) == get_number_of_nodes(deserialized, Sum)
assert get_number_of_nodes(spn, Product) == get_number_of_nodes(
deserialized, Product)
assert get_number_of_nodes(spn, Histogram) == get_number_of_nodes(
deserialized, Histogram)
assert get_number_of_edges(spn) == get_number_of_edges(deserialized)
def histogram_tree_to_spn(tree, features, obj_type, tree_to_spn):
breaks = list(map(ast.literal_eval, tree.children[1].children))
densities = list(map(ast.literal_eval, tree.children[2].children))
bin_repr_points = list(map(ast.literal_eval, tree.children[3].children))
node = Histogram(breaks, densities, bin_repr_points)
feature = str(tree.children[0])
if features is not None:
node.scope.append(features.index(feature))
else:
node.scope.append(int(feature[1:]))
return node
def test_vector_slp_escaping_users():
g0 = Gaussian(mean=0.00, stdev=1, scope=0)
g1 = Gaussian(mean=0.01, stdev=0.75, scope=1)
g2 = Gaussian(mean=0.02, stdev=0.5, scope=2)
g3 = Gaussian(mean=0.03, stdev=0.25, scope=3)
g4 = Gaussian(mean=0.04, stdev=1, scope=4)
g5 = Gaussian(mean=0.05, stdev=0.25, scope=5)
g6 = Gaussian(mean=0.06, stdev=0.5, scope=6)
g7 = Gaussian(mean=0.07, stdev=0.75, scope=7)
g8 = Gaussian(mean=0.08, stdev=1, scope=8)
g9 = Gaussian(mean=0.09, stdev=0.75, scope=9)
g10 = Gaussian(mean=0.10, stdev=1, scope=10)
g11 = Gaussian(mean=0.11, stdev=1, scope=11)
h0 = Histogram([0., 1., 2.], [0.1, 0.9], [1, 1], scope=12)
h1 = Histogram([0., 1., 2.], [0.2, 0.8], [1, 1], scope=13)
h2 = Histogram([0., 1., 2.], [0.3, 0.7], [1, 1], scope=14)
h3 = Histogram([0., 1., 2.], [0.4, 0.6], [1, 1], scope=15)
h4 = Histogram([0., 1., 2.], [0.5, 0.5], [1, 1], scope=16)
h5 = Histogram([0., 1., 2.], [0.6, 0.4], [1, 1], scope=17)
h6 = Histogram([0., 1., 2.], [0.7, 0.3], [1, 1], scope=18)
h7 = Histogram([0., 1., 2.], [0.8, 0.2], [1, 1], scope=19)
c0 = Categorical(p=[0.1, 0.1, 0.8], scope=20)
c1 = Categorical(p=[0.2, 0.2, 0.6], scope=21)
c2 = Categorical(p=[0.3, 0.3, 0.4], scope=22)
c3 = Categorical(p=[0.4, 0.4, 0.2], scope=23)
c4 = Categorical(p=[0.5, 0.4, 0.1], scope=24)
c5 = Categorical(p=[0.6, 0.3, 0.1], scope=25)
c6 = Categorical(p=[0.7, 0.2, 0.1], scope=26)
c7 = Categorical(p=[0.8, 0.1, 0.1], scope=27)
s0 = Sum(children=[g8, h4], weights=[0.5, 0.5])
s1 = Sum(children=[g9, h5], weights=[0.5, 0.5])
s2 = Sum(children=[g10, c6], weights=[0.5, 0.5])
s3 = Sum(children=[g11, h7], weights=[0.5, 0.5])
s4 = Sum(children=[s0, c4], weights=[0.5, 0.5])
s5 = Sum(children=[s1, c5], weights=[0.5, 0.5])
s6 = Sum(children=[s2, g6], weights=[0.5, 0.5])
s7 = Sum(children=[s3, c7], weights=[0.5, 0.5])
s8 = Sum(children=[s4, g4], weights=[0.5, 0.5])
s9 = Sum(children=[s5, g5], weights=[0.5, 0.5])
s10 = Sum(children=[s6, h6], weights=[0.5, 0.5])
s11 = Sum(children=[s7, g7], weights=[0.5, 0.5])
p0 = Product(children=[h0, s8])
p1 = Product(children=[c1, s9])
p2 = Product(children=[c2, s10])
p3 = Product(children=[g3, s11])
p4 = Product(children=[p0, g0])
p5 = Product(children=[p1, g1])
p6 = Product(children=[p2, h2])
p7 = Product(children=[p3, c3])
p8 = Product(children=[p4, c0])
p9 = Product(children=[p5, h1])
p10 = Product(children=[p6, g2])
p11 = Product(children=[p7, h3])
s12 = Sum(children=[p8, p9], weights=[0.5, 0.5])
s13 = Sum(children=[p10, p11], weights=[0.5, 0.5])
s14 = Sum(children=[s12, p2], weights=[0.5, 0.5])
s15 = Sum(children=[s13, s2], weights=[0.5, 0.5])
spn = Product(children=[s14, s15])
# Randomly sample input values from Gaussian (normal) distributions.
num_samples = 100
inputs = np.column_stack((
# gaussian
np.random.normal(loc=0.5, scale=1, size=num_samples),
np.random.normal(loc=0.125, scale=0.25, size=num_samples),
np.random.normal(loc=0.345, scale=0.24, size=num_samples),
np.random.normal(loc=0.456, scale=0.1, size=num_samples),
np.random.normal(loc=0.94, scale=0.48, size=num_samples),
np.random.normal(loc=0.56, scale=0.42, size=num_samples),
np.random.normal(loc=0.76, scale=0.14, size=num_samples),
np.random.normal(loc=0.32, scale=0.58, size=num_samples),
np.random.normal(loc=0.58, scale=0.219, size=num_samples),
np.random.normal(loc=0.14, scale=0.52, size=num_samples),
np.random.normal(loc=0.24, scale=0.42, size=num_samples),
np.random.normal(loc=0.34, scale=0.1, size=num_samples),
# histogram
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
np.random.randint(low=0, high=2, size=num_samples),
# categorical
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples),
np.random.randint(low=0, high=3, size=num_samples))).astype("float64")
# Compute the reference results using the inference from SPFlow.
reference = log_likelihood(spn, inputs)
reference = reference.reshape(num_samples)
# Compile the kernel with batch size 1 to enable SLP vectorization.
compiler = CPUCompiler(vectorize=True,
computeInLogSpace=True,
vectorLibrary="LIBMVEC")
kernel = compiler.compile_ll(spn=spn, batchSize=1, supportMarginal=False)
# Execute the compiled Kernel.
time_sum = 0
for i in range(len(reference)):
# Check the computation results against the reference
start = time.time()
result = compiler.execute(kernel, inputs=np.array([inputs[i]]))
time_sum = time_sum + time.time() - start
print(
f"evaluation #{i}: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}",
end='\r')
if not np.isclose(result, reference[i]):
print(
f"\nevaluation #{i} failed: result: {result[0]:16.8f}, reference: {reference[i]:16.8f}"
)
raise AssertionError()
print(f"\nExecution of {len(reference)} samples took {time_sum} seconds.")